8 papers · 1 filter
On the Stability and Realizability of Recurrent Polynomial Surrogate Ternary Logic Gate Networks
Sai Sandeep Damera, Ryan Matheu, Aniruddh G. Puranic +2
Recurrent Neural Networks (RNNs) can learn to predict Signal Temporal Logic (STL) verdicts online from partial trajectories, but deploying them as runtime monitors in safety-critic…
Learning from Imperfect Demonstrations via Temporal Behavior Tree-Guided Trajectory Repair
Aniruddh G. Puranic, Sebastian Schirmer, John S. Baras +1
Learning robot control policies from demonstrations is a powerful paradigm, yet real-world data is often suboptimal, noisy, or otherwise imperfect, posing significant challenges fo…
Accelerated Learning with Linear Temporal Logic using Differentiable Simulation
Alper Kamil Bozkurt, Calin Belta, Ming C. Lin
Ensuring that reinforcement learning (RL) controllers satisfy safety and reliability constraints in real-world settings remains challenging: state-avoidance and constrained Markov…
Safety-Aware Reinforcement Learning for Control via Risk-Sensitive Action-Value Iteration and Quantile Regression
Clinton Enwerem, Aniruddh G. Puranic, John S. Baras +1
Mainstream approximate action-value iteration reinforcement learning (RL) algorithms suffer from overestimation bias, leading to suboptimal policies in high-variance stochastic env…
STL-based Optimization of Biomolecular Neural Networks for Regression and Control
Eric Palanques-Tost, Hanna Krasowski, Murat Arcak +2
Biomolecular Neural Networks (BNNs), artificial neural networks with biologically synthesizable architectures, achieve universal function approximation capabilities beyond simple b…
Interpretable Imitation Learning via Generative Adversarial STL Inference and Control
Wenliang Liu, Danyang Li, Erfan Aasi +3
Imitation learning methods have demonstrated considerable success in teaching autonomous systems complex tasks through expert demonstrations. However, a limitation of these methods…